Русский: README.ru.md · 中文: README.zh.md

CMF — Cortiq Model Format

CMF is an auditable model container: one file can hold weights, tokenizer, chat metadata, task masks and skill overlays for inference without a large framework runtime.

Status

CMF v2 is the current on-disk format. Readers validate the envelope, section bounds, tensor metadata and hashes; incompatible changes require a feature bit or version bump. The Rust crate APIs are still pre-1.0 and may change.

The project is usable for local inference and format experiments. Treat model quality and speed numbers as workload-specific measurements, not guarantees; see the focused guides for the test setup behind each claim.

Quick start

Install the CLI and convert a small public checkpoint:

cargo install cortiq-cli
cortiq convert --model Qwen/Qwen3-0.6B --quant q8 --output qwen.cmf
cortiq run qwen.cmf --prompt "What is the capital of France?" --greedy --no-think

Already have GGUF? Import it directly:

cortiq import-gguf model.gguf --output model.cmf
cortiq verify model.cmf

The CLI also exposes info, bench, ppl, serve, skill, moe-mask, moe-defrag, requant, compact, sign, imagine, animate and ltx-video. Run cortiq <command> --help for flags and current limitations.

What a CMF file contains

• A fixed 128-byte envelope addressing every section.
• Header JSON with architecture, quantization defaults, chat metadata and provenance.
• A binary tensor directory with dtype, shape, offsets, lengths and hash64.
• A page-aligned weight blob that can be memory-mapped and read in place.
• Optional task masks, skill replacement tensors, tokenizer bytes and sparse indexes.

cortiq verify fails on malformed bounds or a hash mismatch; python/cmf_reader.py is a small independent reader for inspection. The normative layout is in the CMF v2 specification.

Quantization

Quantization is selected per tensor, so sensitive tensors can stay at a higher precision while large matrix blocks use a compact codec.

See Q1T/PTQ for the experimental low-bit path and the quantization coverage matrix for codec support by execution path.

Runtime capabilities

• CPU inference: portable Rust implementation with memory-mapped weights.
• GPU inference: native Metal on macOS and wgpu backends (Vulkan/DX12) where the device and build support them. Use CMF_GPU=1 to request GPU.
• Long context: optional --o1 attention uses fixed-size state and trades memory growth for a measured quality delta; measure on your model.
• Skills: one backbone can carry task masks and replacement-tensor overlays; inactive overlays do not need a second full model copy.
• MoE: task masks and physical defragmentation can reduce the active expert set. Validate perplexity on held-out data before shipping a restriction.
• Speculative decode: supported MTP/draft paths are enabled only where the model metadata and measured acceptance make them useful.
• Serving: cortiq serve provides an OpenAI-compatible local HTTP API.
• Media: imagine, animate and ltx-video pack model assets and run image, video or audio-capable pipelines when their model guides apply.

Platforms and model families

Native conversion covers Qwen, Llama, Mistral, Gemma, Phi, DeepSeek, Kimi Linear, MiniCPM and several MoE/video families. Model-specific constraints and published files are indexed in the model guides. For an unsupported checkpoint, try import-gguf and file a reproducible issue if it fails.

Focused documentation

• CMF v2 specification — normative file layout.
• Format comparison — criteria and evidence.
• Skills — masks, overlays and routing workflows.
• GPU kernel recipes — backend measurements.
• Multi-GPU execution and mobile split.
• FCD restoration and low-bit PTQ.
• Model cards and conversion notes.
• Cortiq Spectra — deterministic CPU streaming colorization for dual-energy X-ray captures, scanner profiles, refusal masks, and measured limits (Russian).

Build from source

cargo build --release --workspace
cargo build --release -p cortiq-cli --no-default-features  # CPU-only CLI
cargo test --workspace

The workspace is Apache-2.0. Read LICENSE, PATENTS.md, CONTRIBUTING.md and SECURITY.md before redistributing or reporting a vulnerability. Releases and checksums are listed on the GitHub releases page.